Overview

Dataset statistics

Number of variables24
Number of observations2693
Missing cells2440
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory505.1 KiB
Average record size in memory192.0 B

Variable types

Numeric11
Categorical9
Boolean4

Alerts

host_name has a high cardinality: 1046 distinct values High cardinality
host_since has a high cardinality: 1189 distinct values High cardinality
host_acceptance_rate has a high cardinality: 61 distinct values High cardinality
host_verifications has a high cardinality: 185 distinct values High cardinality
amenities has a high cardinality: 2631 distinct values High cardinality
id is highly correlated with host_idHigh correlation
accommodates is highly correlated with room_type and 2 other fieldsHigh correlation
bedrooms is highly correlated with room_type and 2 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
reviews_per_month is highly correlated with number_of_reviewsHigh correlation
host_response_time is highly correlated with host_response_rate and 1 other fieldsHigh correlation
host_response_rate is highly correlated with host_id and 5 other fieldsHigh correlation
host_id is highly correlated with id and 2 other fieldsHigh correlation
host_acceptance_rate is highly correlated with host_id and 5 other fieldsHigh correlation
neighbourhood is highly correlated with host_response_rate and 1 other fieldsHigh correlation
room_type is highly correlated with host_response_rate and 3 other fieldsHigh correlation
minimum_nights is highly correlated with host_acceptance_rateHigh correlation
has_availability is highly correlated with host_response_rateHigh correlation
host_response_time has 561 (20.8%) missing values Missing
host_response_rate has 561 (20.8%) missing values Missing
host_acceptance_rate has 306 (11.4%) missing values Missing
bedrooms has 164 (6.1%) missing values Missing
beds has 34 (1.3%) missing values Missing
review_scores_rating has 414 (15.4%) missing values Missing
reviews_per_month has 400 (14.9%) missing values Missing
amenities is uniformly distributed Uniform
id has unique values Unique
beds has 87 (3.2%) zeros Zeros
availability_365 has 366 (13.6%) zeros Zeros
number_of_reviews has 400 (14.9%) zeros Zeros

Reproduction

Analysis started2022-10-25 03:37:39.247721
Analysis finished2022-10-25 03:38:10.881451
Duration31.63 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct2693
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27091380.81
Minimum12651
Maximum47299925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2022-10-25T03:38:11.019262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12651
5-th percentile2293877
Q116299270
median28749977
Q339469121
95-th percentile45971436
Maximum47299925
Range47287274
Interquartile range (IQR)23169851

Descriptive statistics

Standard deviation13789171.58
Coefficient of variation (CV)0.5089874035
Kurtosis-1.075274055
Mean27091380.81
Median Absolute Deviation (MAD)11191580
Skewness-0.3160441557
Sum7.295708851 × 1010
Variance1.901412527 × 1014
MonotonicityStrictly increasing
2022-10-25T03:38:11.317103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126511
 
< 0.1%
361811281
 
< 0.1%
360460931
 
< 0.1%
360748421
 
< 0.1%
360748991
 
< 0.1%
360749741
 
< 0.1%
361012481
 
< 0.1%
361160051
 
< 0.1%
361468761
 
< 0.1%
361803131
 
< 0.1%
Other values (2683)2683
99.6%
ValueCountFrequency (%)
126511
< 0.1%
393421
< 0.1%
405601
< 0.1%
445041
< 0.1%
452871
< 0.1%
454441
< 0.1%
553011
< 0.1%
631461
< 0.1%
722111
< 0.1%
835841
< 0.1%
ValueCountFrequency (%)
472999251
< 0.1%
472950171
< 0.1%
472918531
< 0.1%
472747721
< 0.1%
472746371
< 0.1%
472744451
< 0.1%
472743591
< 0.1%
472742511
< 0.1%
472700341
< 0.1%
472687621
< 0.1%

host_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1515
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91811769
Minimum249
Maximum382559015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2022-10-25T03:38:11.536618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum249
5-th percentile1130179
Q113635140
median48005494
Q3147286744
95-th percentile302759750
Maximum382559015
Range382558766
Interquartile range (IQR)133651604

Descriptive statistics

Standard deviation99219007.04
Coefficient of variation (CV)1.080678524
Kurtosis0.2644001559
Mean91811769
Median Absolute Deviation (MAD)43195618
Skewness1.159531113
Sum2.472490939 × 1011
Variance9.844411358 × 1015
MonotonicityNot monotonic
2022-10-25T03:38:11.836156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4800549456
 
2.1%
4380631
 
1.2%
9428245324
 
0.9%
1883943421
 
0.8%
941968420
 
0.7%
26922126019
 
0.7%
3816307219
 
0.7%
601740717
 
0.6%
4157458716
 
0.6%
291774416
 
0.6%
Other values (1505)2454
91.1%
ValueCountFrequency (%)
2491
 
< 0.1%
125611
 
< 0.1%
4380631
1.2%
497353
 
0.1%
528352
 
0.1%
718121
 
< 0.1%
878892
 
0.1%
895181
 
< 0.1%
925291
 
< 0.1%
1005361
 
< 0.1%
ValueCountFrequency (%)
3825590157
0.3%
3806419281
 
< 0.1%
3803090402
 
0.1%
3798251131
 
< 0.1%
3767630792
 
0.1%
3754278363
0.1%
3752045095
0.2%
3750748971
 
< 0.1%
3739667771
 
< 0.1%
3703185181
 
< 0.1%

host_name
Categorical

HIGH CARDINALITY

Distinct1046
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
Zeus
 
56
Vic
 
31
Startup House
 
27
Alex
 
27
Kimberly
 
25
Other values (1041)
2527 

Length

Max length35
Median length31
Mean length6.672855551
Min length1

Characters and Unicode

Total characters17970
Distinct characters71
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique581 ?
Unique (%)21.6%

Sample

1st rowLaurel
2nd rowAnnette
3rd rowMegan
4th rowMaggie
5th rowDiane And Mike

Common Values

ValueCountFrequency (%)
Zeus56
 
2.1%
Vic31
 
1.2%
Startup House27
 
1.0%
Alex27
 
1.0%
Kimberly25
 
0.9%
Kia23
 
0.9%
Tribe21
 
0.8%
John20
 
0.7%
Churchill20
 
0.7%
Evgeny19
 
0.7%
Other values (1036)2424
90.0%

Length

2022-10-25T03:38:12.135911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
79
 
2.4%
and69
 
2.1%
zeus56
 
1.7%
vic31
 
0.9%
alex30
 
0.9%
house27
 
0.8%
startup27
 
0.8%
kimberly26
 
0.8%
kia23
 
0.7%
john22
 
0.7%
Other values (1060)2905
88.2%

Most occurring characters

ValueCountFrequency (%)
a1929
 
10.7%
e1720
 
9.6%
n1520
 
8.5%
i1438
 
8.0%
r1088
 
6.1%
l951
 
5.3%
o671
 
3.7%
621
 
3.5%
t576
 
3.2%
s544
 
3.0%
Other values (61)6912
38.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13890
77.3%
Uppercase Letter3289
 
18.3%
Space Separator621
 
3.5%
Other Punctuation98
 
0.5%
Open Punctuation23
 
0.1%
Close Punctuation23
 
0.1%
Dash Punctuation11
 
0.1%
Decimal Number11
 
0.1%
Other Letter3
 
< 0.1%
Math Symbol1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1929
13.9%
e1720
12.4%
n1520
10.9%
i1438
10.4%
r1088
 
7.8%
l951
 
6.8%
o671
 
4.8%
t576
 
4.1%
s544
 
3.9%
u520
 
3.7%
Other values (20)2933
21.1%
Uppercase Letter
ValueCountFrequency (%)
A337
 
10.2%
M322
 
9.8%
J274
 
8.3%
S246
 
7.5%
C198
 
6.0%
L195
 
5.9%
K175
 
5.3%
E171
 
5.2%
R139
 
4.2%
V127
 
3.9%
Other values (16)1105
33.6%
Other Punctuation
ValueCountFrequency (%)
&73
74.5%
'16
 
16.3%
.8
 
8.2%
/1
 
1.0%
Decimal Number
ValueCountFrequency (%)
59
81.8%
21
 
9.1%
41
 
9.1%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Space Separator
ValueCountFrequency (%)
621
100.0%
Open Punctuation
ValueCountFrequency (%)
(23
100.0%
Close Punctuation
ValueCountFrequency (%)
)23
100.0%
Dash Punctuation
ValueCountFrequency (%)
-11
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17179
95.6%
Common788
 
4.4%
Han3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1929
 
11.2%
e1720
 
10.0%
n1520
 
8.8%
i1438
 
8.4%
r1088
 
6.3%
l951
 
5.5%
o671
 
3.9%
t576
 
3.4%
s544
 
3.2%
u520
 
3.0%
Other values (46)6222
36.2%
Common
ValueCountFrequency (%)
621
78.8%
&73
 
9.3%
(23
 
2.9%
)23
 
2.9%
'16
 
2.0%
-11
 
1.4%
59
 
1.1%
.8
 
1.0%
/1
 
0.1%
21
 
0.1%
Other values (2)2
 
0.3%
Han
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII17961
99.9%
None6
 
< 0.1%
CJK3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1929
 
10.7%
e1720
 
9.6%
n1520
 
8.5%
i1438
 
8.0%
r1088
 
6.1%
l951
 
5.3%
o671
 
3.7%
621
 
3.5%
t576
 
3.2%
s544
 
3.0%
Other values (54)6903
38.4%
None
ValueCountFrequency (%)
è3
50.0%
ê1
 
16.7%
ü1
 
16.7%
ş1
 
16.7%
CJK
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

host_since
Categorical

HIGH CARDINALITY

Distinct1189
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
11/2/15
 
56
10/6/09
 
31
6/17/19
 
27
9/9/16
 
24
7/24/14
 
21
Other values (1184)
2534 

Length

Max length8
Median length7
Mean length6.947642035
Min length6

Characters and Unicode

Total characters18710
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique665 ?
Unique (%)24.7%

Sample

1st row10/29/09
2nd row5/18/10
3rd row7/20/10
4th row8/8/10
5th row8/13/10

Common Values

ValueCountFrequency (%)
11/2/1556
 
2.1%
10/6/0931
 
1.2%
6/17/1927
 
1.0%
9/9/1624
 
0.9%
7/24/1421
 
0.8%
10/14/1320
 
0.7%
7/12/1219
 
0.7%
7/11/1519
 
0.7%
4/20/1318
 
0.7%
11/19/1816
 
0.6%
Other values (1179)2442
90.7%

Length

2022-10-25T03:38:12.434601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11/2/1556
 
2.1%
10/6/0931
 
1.2%
6/17/1927
 
1.0%
9/9/1624
 
0.9%
7/24/1421
 
0.8%
10/14/1320
 
0.7%
7/12/1219
 
0.7%
7/11/1519
 
0.7%
4/20/1318
 
0.7%
11/19/1816
 
0.6%
Other values (1179)2442
90.7%

Most occurring characters

ValueCountFrequency (%)
/5386
28.8%
15029
26.9%
21797
 
9.6%
6988
 
5.3%
5962
 
5.1%
9863
 
4.6%
3812
 
4.3%
4783
 
4.2%
7782
 
4.2%
8694
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number13324
71.2%
Other Punctuation5386
28.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
15029
37.7%
21797
 
13.5%
6988
 
7.4%
5962
 
7.2%
9863
 
6.5%
3812
 
6.1%
4783
 
5.9%
7782
 
5.9%
8694
 
5.2%
0614
 
4.6%
Other Punctuation
ValueCountFrequency (%)
/5386
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common18710
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/5386
28.8%
15029
26.9%
21797
 
9.6%
6988
 
5.3%
5962
 
5.1%
9863
 
4.6%
3812
 
4.3%
4783
 
4.2%
7782
 
4.2%
8694
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII18710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/5386
28.8%
15029
26.9%
21797
 
9.6%
6988
 
5.3%
5962
 
5.1%
9863
 
4.6%
3812
 
4.3%
4783
 
4.2%
7782
 
4.2%
8694
 
3.7%

host_response_time
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.2%
Missing561
Missing (%)20.8%
Memory size21.2 KiB
within an hour
1572 
within a few hours
333 
within a day
182 
a few days or more
 
45

Length

Max length18
Median length14
Mean length14.53846154
Min length12

Characters and Unicode

Total characters30996
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwithin an hour
2nd rowwithin a few hours
3rd rowwithin an hour
4th rowwithin an hour
5th rowwithin an hour

Common Values

ValueCountFrequency (%)
within an hour1572
58.4%
within a few hours333
 
12.4%
within a day182
 
6.8%
a few days or more45
 
1.7%
(Missing)561
 
20.8%

Length

2022-10-25T03:38:12.723823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T03:38:13.018105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
within2087
30.6%
an1572
23.1%
hour1572
23.1%
a560
 
8.2%
few378
 
5.5%
hours333
 
4.9%
day182
 
2.7%
days45
 
0.7%
or45
 
0.7%
more45
 
0.7%

Most occurring characters

ValueCountFrequency (%)
4687
15.1%
i4174
13.5%
h3992
12.9%
n3659
11.8%
w2465
8.0%
a2359
7.6%
t2087
6.7%
o1995
6.4%
r1995
6.4%
u1905
6.1%
Other values (6)1678
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter26309
84.9%
Space Separator4687
 
15.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i4174
15.9%
h3992
15.2%
n3659
13.9%
w2465
9.4%
a2359
9.0%
t2087
7.9%
o1995
7.6%
r1995
7.6%
u1905
7.2%
e423
 
1.6%
Other values (5)1255
 
4.8%
Space Separator
ValueCountFrequency (%)
4687
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin26309
84.9%
Common4687
 
15.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i4174
15.9%
h3992
15.2%
n3659
13.9%
w2465
9.4%
a2359
9.0%
t2087
7.9%
o1995
7.6%
r1995
7.6%
u1905
7.2%
e423
 
1.6%
Other values (5)1255
 
4.8%
Common
ValueCountFrequency (%)
4687
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4687
15.1%
i4174
13.5%
h3992
12.9%
n3659
11.8%
w2465
8.0%
a2359
7.6%
t2087
6.7%
o1995
6.4%
r1995
6.4%
u1905
6.1%
Other values (6)1678
 
5.4%

host_response_rate
Categorical

HIGH CORRELATION
MISSING

Distinct35
Distinct (%)1.6%
Missing561
Missing (%)20.8%
Memory size21.2 KiB
100%
1646 
97%
 
65
90%
 
45
91%
 
34
94%
 
32
Other values (30)
310 

Length

Max length4
Median length4
Mean length3.760787992
Min length2

Characters and Unicode

Total characters8018
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st row100%
2nd row100%
3rd row100%
4th row100%
5th row100%

Common Values

ValueCountFrequency (%)
100%1646
61.1%
97%65
 
2.4%
90%45
 
1.7%
91%34
 
1.3%
94%32
 
1.2%
99%30
 
1.1%
50%29
 
1.1%
80%24
 
0.9%
0%24
 
0.9%
96%22
 
0.8%
Other values (25)181
 
6.7%
(Missing)561
 
20.8%

Length

2022-10-25T03:38:13.216943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1001646
77.2%
9765
 
3.0%
9045
 
2.1%
9134
 
1.6%
9432
 
1.5%
9930
 
1.4%
5029
 
1.4%
024
 
1.1%
8024
 
1.1%
9622
 
1.0%
Other values (25)181
 
8.5%

Most occurring characters

ValueCountFrequency (%)
03430
42.8%
%2132
26.6%
11688
21.1%
9325
 
4.1%
8101
 
1.3%
791
 
1.1%
685
 
1.1%
571
 
0.9%
346
 
0.6%
438
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5886
73.4%
Other Punctuation2132
 
26.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03430
58.3%
11688
28.7%
9325
 
5.5%
8101
 
1.7%
791
 
1.5%
685
 
1.4%
571
 
1.2%
346
 
0.8%
438
 
0.6%
211
 
0.2%
Other Punctuation
ValueCountFrequency (%)
%2132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8018
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03430
42.8%
%2132
26.6%
11688
21.1%
9325
 
4.1%
8101
 
1.3%
791
 
1.1%
685
 
1.1%
571
 
0.9%
346
 
0.6%
438
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII8018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03430
42.8%
%2132
26.6%
11688
21.1%
9325
 
4.1%
8101
 
1.3%
791
 
1.1%
685
 
1.1%
571
 
0.9%
346
 
0.6%
438
 
0.5%

host_acceptance_rate
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct61
Distinct (%)2.6%
Missing306
Missing (%)11.4%
Memory size21.2 KiB
100%
893 
99%
244 
98%
149 
97%
122 
95%
 
82
Other values (56)
897 

Length

Max length4
Median length3
Mean length3.344784248
Min length2

Characters and Unicode

Total characters7984
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.3%

Sample

1st row98%
2nd row40%
3rd row48%
4th row100%
5th row94%

Common Values

ValueCountFrequency (%)
100%893
33.2%
99%244
 
9.1%
98%149
 
5.5%
97%122
 
4.5%
95%82
 
3.0%
90%80
 
3.0%
0%70
 
2.6%
96%66
 
2.5%
86%53
 
2.0%
92%47
 
1.7%
Other values (51)581
21.6%
(Missing)306
 
11.4%

Length

2022-10-25T03:38:13.440964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100893
37.4%
99244
 
10.2%
98149
 
6.2%
97122
 
5.1%
9582
 
3.4%
9080
 
3.4%
070
 
2.9%
9666
 
2.8%
8653
 
2.2%
9247
 
2.0%
Other values (51)581
24.3%

Most occurring characters

ValueCountFrequency (%)
%2387
29.9%
02033
25.5%
91211
15.2%
1952
 
11.9%
8422
 
5.3%
6259
 
3.2%
7254
 
3.2%
5174
 
2.2%
3119
 
1.5%
295
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5597
70.1%
Other Punctuation2387
29.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02033
36.3%
91211
21.6%
1952
17.0%
8422
 
7.5%
6259
 
4.6%
7254
 
4.5%
5174
 
3.1%
3119
 
2.1%
295
 
1.7%
478
 
1.4%
Other Punctuation
ValueCountFrequency (%)
%2387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7984
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
%2387
29.9%
02033
25.5%
91211
15.2%
1952
 
11.9%
8422
 
5.3%
6259
 
3.2%
7254
 
3.2%
5174
 
2.2%
3119
 
1.5%
295
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII7984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
%2387
29.9%
02033
25.5%
91211
15.2%
1952
 
11.9%
8422
 
5.3%
6259
 
3.2%
7254
 
3.2%
5174
 
2.2%
3119
 
1.5%
295
 
1.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
False
1488 
True
1205 
ValueCountFrequency (%)
False1488
55.3%
True1205
44.7%
2022-10-25T03:38:13.725352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

host_verifications
Categorical

HIGH CARDINALITY

Distinct185
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
['email', 'phone', 'reviews', 'kba']
198 
['email', 'phone']
190 
['email', 'phone', 'reviews', 'jumio', 'offline_government_id', 'selfie', 'government_id', 'identity_manual']
 
142
['email', 'phone', 'reviews', 'jumio', 'government_id']
 
140
['email', 'phone', 'reviews', 'jumio', 'offline_government_id', 'government_id']
 
130
Other values (180)
1893 

Length

Max length165
Median length128
Mean length69.27664315
Min length7

Characters and Unicode

Total characters186562
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)2.3%

Sample

1st row['email', 'phone', 'facebook', 'reviews', 'kba']
2nd row['email', 'phone', 'reviews', 'offline_government_id', 'kba', 'government_id']
3rd row['email', 'phone', 'reviews', 'jumio', 'offline_government_id', 'government_id']
4th row['email', 'phone', 'facebook', 'reviews', 'offline_government_id', 'kba', 'selfie', 'government_id', 'identity_manual', 'work_email']
5th row['email', 'phone', 'reviews', 'kba']

Common Values

ValueCountFrequency (%)
['email', 'phone', 'reviews', 'kba']198
 
7.4%
['email', 'phone']190
 
7.1%
['email', 'phone', 'reviews', 'jumio', 'offline_government_id', 'selfie', 'government_id', 'identity_manual']142
 
5.3%
['email', 'phone', 'reviews', 'jumio', 'government_id']140
 
5.2%
['email', 'phone', 'reviews', 'jumio', 'offline_government_id', 'government_id']130
 
4.8%
['email', 'phone', 'reviews']130
 
4.8%
['email', 'phone', 'offline_government_id', 'government_id']130
 
4.8%
['email', 'phone', 'reviews', 'jumio', 'government_id', 'work_email']111
 
4.1%
['email', 'phone', 'offline_government_id', 'selfie', 'government_id', 'identity_manual']90
 
3.3%
['email', 'phone', 'reviews', 'jumio', 'offline_government_id', 'government_id', 'work_email']68
 
2.5%
Other values (175)1364
50.6%

Length

2022-10-25T03:38:14.017076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
phone2689
18.1%
email2525
17.0%
reviews1929
13.0%
government_id1867
12.5%
offline_government_id1421
9.5%
jumio1183
8.0%
selfie785
 
5.3%
identity_manual665
 
4.5%
kba593
 
4.0%
work_email520
 
3.5%
Other values (9)703
 
4.7%

Most occurring characters

ValueCountFrequency (%)
'29760
16.0%
e20535
 
11.0%
i13102
 
7.0%
n12262
 
6.6%
,12187
 
6.5%
12187
 
6.5%
o10366
 
5.6%
m8280
 
4.4%
l6278
 
3.4%
_6007
 
3.2%
Other values (20)55598
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter121035
64.9%
Other Punctuation41947
 
22.5%
Space Separator12187
 
6.5%
Connector Punctuation6007
 
3.2%
Close Punctuation2693
 
1.4%
Open Punctuation2693
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e20535
17.0%
i13102
10.8%
n12262
10.1%
o10366
 
8.6%
m8280
 
6.8%
l6278
 
5.2%
r5737
 
4.7%
a5570
 
4.6%
v5217
 
4.3%
t4634
 
3.8%
Other values (14)29054
24.0%
Other Punctuation
ValueCountFrequency (%)
'29760
70.9%
,12187
29.1%
Space Separator
ValueCountFrequency (%)
12187
100.0%
Connector Punctuation
ValueCountFrequency (%)
_6007
100.0%
Close Punctuation
ValueCountFrequency (%)
]2693
100.0%
Open Punctuation
ValueCountFrequency (%)
[2693
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin121035
64.9%
Common65527
35.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e20535
17.0%
i13102
10.8%
n12262
10.1%
o10366
 
8.6%
m8280
 
6.8%
l6278
 
5.2%
r5737
 
4.7%
a5570
 
4.6%
v5217
 
4.3%
t4634
 
3.8%
Other values (14)29054
24.0%
Common
ValueCountFrequency (%)
'29760
45.4%
,12187
18.6%
12187
18.6%
_6007
 
9.2%
]2693
 
4.1%
[2693
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII186562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'29760
16.0%
e20535
 
11.0%
i13102
 
7.0%
n12262
 
6.6%
,12187
 
6.5%
12187
 
6.5%
o10366
 
5.6%
m8280
 
4.4%
l6278
 
3.4%
_6007
 
3.2%
Other values (20)55598
29.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
True
2174 
False
519 
ValueCountFrequency (%)
True2174
80.7%
False519
 
19.3%
2022-10-25T03:38:14.240793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

neighbourhood
Categorical

HIGH CORRELATION

Distinct21
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
Daly City
455 
Unincorporated Areas
316 
San Mateo
288 
Menlo Park
249 
Redwood City
228 
Other values (16)
1157 

Length

Max length20
Median length14
Mean length11.64054957
Min length5

Characters and Unicode

Total characters31348
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMenlo Park
2nd rowPacifica
3rd rowEast Palo Alto
4th rowBurlingame
5th rowBurlingame

Common Values

ValueCountFrequency (%)
Daly City455
16.9%
Unincorporated Areas316
11.7%
San Mateo288
10.7%
Menlo Park249
9.2%
Redwood City228
8.5%
East Palo Alto185
6.9%
South San Francisco166
 
6.2%
Pacifica137
 
5.1%
San Bruno118
 
4.4%
Burlingame102
 
3.8%
Other values (11)449
16.7%

Length

2022-10-25T03:38:14.438560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city738
14.0%
san652
12.3%
daly455
 
8.6%
unincorporated316
 
6.0%
areas316
 
6.0%
mateo288
 
5.4%
menlo249
 
4.7%
park249
 
4.7%
redwood228
 
4.3%
east185
 
3.5%
Other values (21)1614
30.5%

Most occurring characters

ValueCountFrequency (%)
a3492
 
11.1%
o2897
 
9.2%
2597
 
8.3%
n2090
 
6.7%
t2073
 
6.6%
r1883
 
6.0%
e1788
 
5.7%
i1747
 
5.6%
l1590
 
5.1%
y1249
 
4.0%
Other values (26)9942
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23461
74.8%
Uppercase Letter5290
 
16.9%
Space Separator2597
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a3492
14.9%
o2897
12.3%
n2090
8.9%
t2073
8.8%
r1883
8.0%
e1788
7.6%
i1747
7.4%
l1590
6.8%
y1249
 
5.3%
c922
 
3.9%
Other values (11)3730
15.9%
Uppercase Letter
ValueCountFrequency (%)
C828
15.7%
S818
15.5%
M665
12.6%
P577
10.9%
A538
10.2%
D455
8.6%
B354
6.7%
U316
 
6.0%
R228
 
4.3%
F221
 
4.2%
Other values (4)290
 
5.5%
Space Separator
ValueCountFrequency (%)
2597
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28751
91.7%
Common2597
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a3492
 
12.1%
o2897
 
10.1%
n2090
 
7.3%
t2073
 
7.2%
r1883
 
6.5%
e1788
 
6.2%
i1747
 
6.1%
l1590
 
5.5%
y1249
 
4.3%
c922
 
3.2%
Other values (25)9020
31.4%
Common
ValueCountFrequency (%)
2597
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII31348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a3492
 
11.1%
o2897
 
9.2%
2597
 
8.3%
n2090
 
6.7%
t2073
 
6.6%
r1883
 
6.0%
e1788
 
5.7%
i1747
 
5.6%
l1590
 
5.1%
y1249
 
4.0%
Other values (26)9942
31.7%

room_type
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
Entire home/apt
1571 
Private room
980 
Shared room
 
139
Hotel room
 
3

Length

Max length15
Median length15
Mean length13.69624954
Min length10

Characters and Unicode

Total characters36884
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowPrivate room
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt1571
58.3%
Private room980
36.4%
Shared room139
 
5.2%
Hotel room3
 
0.1%

Length

2022-10-25T03:38:14.728345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T03:38:14.947779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
entire1571
29.2%
home/apt1571
29.2%
room1122
20.8%
private980
18.2%
shared139
 
2.6%
hotel3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e4264
11.6%
t4125
11.2%
o3818
10.4%
r3812
10.3%
m2693
 
7.3%
2693
 
7.3%
a2690
 
7.3%
i2551
 
6.9%
h1710
 
4.6%
p1571
 
4.3%
Other values (9)6957
18.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29927
81.1%
Space Separator2693
 
7.3%
Uppercase Letter2693
 
7.3%
Other Punctuation1571
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4264
14.2%
t4125
13.8%
o3818
12.8%
r3812
12.7%
m2693
9.0%
a2690
9.0%
i2551
8.5%
h1710
5.7%
p1571
 
5.2%
n1571
 
5.2%
Other values (3)1122
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
E1571
58.3%
P980
36.4%
S139
 
5.2%
H3
 
0.1%
Space Separator
ValueCountFrequency (%)
2693
100.0%
Other Punctuation
ValueCountFrequency (%)
/1571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin32620
88.4%
Common4264
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4264
13.1%
t4125
12.6%
o3818
11.7%
r3812
11.7%
m2693
8.3%
a2690
8.2%
i2551
7.8%
h1710
5.2%
p1571
 
4.8%
E1571
 
4.8%
Other values (7)3815
11.7%
Common
ValueCountFrequency (%)
2693
63.2%
/1571
36.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII36884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4264
11.6%
t4125
11.2%
o3818
10.4%
r3812
10.3%
m2693
 
7.3%
2693
 
7.3%
a2690
 
7.3%
i2551
 
6.9%
h1710
 
4.6%
p1571
 
4.3%
Other values (9)6957
18.9%

accommodates
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.869662087
Minimum0
Maximum16
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2022-10-25T03:38:15.126107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q35
95-th percentile10
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.156236979
Coefficient of variation (CV)0.8156363291
Kurtosis3.52192029
Mean3.869662087
Median Absolute Deviation (MAD)1
Skewness1.820609723
Sum10421
Variance9.961831869
MonotonicityNot monotonic
2022-10-25T03:38:15.346739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2936
34.8%
1426
15.8%
4363
 
13.5%
6235
 
8.7%
3192
 
7.1%
5151
 
5.6%
8121
 
4.5%
1057
 
2.1%
756
 
2.1%
1648
 
1.8%
Other values (7)108
 
4.0%
ValueCountFrequency (%)
02
 
0.1%
1426
15.8%
2936
34.8%
3192
 
7.1%
4363
 
13.5%
5151
 
5.6%
6235
 
8.7%
756
 
2.1%
8121
 
4.5%
930
 
1.1%
ValueCountFrequency (%)
1648
 
1.8%
159
 
0.3%
1414
 
0.5%
135
 
0.2%
1238
 
1.4%
1110
 
0.4%
1057
2.1%
930
 
1.1%
8121
4.5%
756
2.1%

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.3%
Missing164
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean1.70897588
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2022-10-25T03:38:15.617141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.156607026
Coefficient of variation (CV)0.6767837041
Kurtosis3.618077353
Mean1.70897588
Median Absolute Deviation (MAD)0
Skewness1.881852762
Sum4322
Variance1.337739813
MonotonicityNot monotonic
2022-10-25T03:38:15.833683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
11600
59.4%
2433
 
16.1%
3266
 
9.9%
4134
 
5.0%
568
 
2.5%
618
 
0.7%
76
 
0.2%
84
 
0.1%
(Missing)164
 
6.1%
ValueCountFrequency (%)
11600
59.4%
2433
 
16.1%
3266
 
9.9%
4134
 
5.0%
568
 
2.5%
618
 
0.7%
76
 
0.2%
84
 
0.1%
ValueCountFrequency (%)
84
 
0.1%
76
 
0.2%
618
 
0.7%
568
 
2.5%
4134
 
5.0%
3266
 
9.9%
2433
 
16.1%
11600
59.4%

beds
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct21
Distinct (%)0.8%
Missing34
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean2.21963144
Minimum0
Maximum27
Zeros87
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2022-10-25T03:38:16.060750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum27
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.031332377
Coefficient of variation (CV)0.9151665182
Kurtosis20.02815135
Mean2.21963144
Median Absolute Deviation (MAD)1
Skewness3.214001409
Sum5902
Variance4.126311226
MonotonicityNot monotonic
2022-10-25T03:38:16.331652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
11275
47.3%
2506
 
18.8%
3316
 
11.7%
4203
 
7.5%
599
 
3.7%
087
 
3.2%
672
 
2.7%
834
 
1.3%
729
 
1.1%
911
 
0.4%
Other values (11)27
 
1.0%
(Missing)34
 
1.3%
ValueCountFrequency (%)
087
 
3.2%
11275
47.3%
2506
 
18.8%
3316
 
11.7%
4203
 
7.5%
599
 
3.7%
672
 
2.7%
729
 
1.1%
834
 
1.3%
911
 
0.4%
ValueCountFrequency (%)
271
 
< 0.1%
231
 
< 0.1%
201
 
< 0.1%
171
 
< 0.1%
161
 
< 0.1%
152
 
0.1%
141
 
< 0.1%
131
 
< 0.1%
125
0.2%
114
0.1%

amenities
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2631
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
["Refrigerator", "Hot water", "Dishes and silverware", "Patio or balcony", "Bed linens", "Oven", "Hair dryer", "Extra pillows and blankets", "Long term stays allowed", "Hangers", "Smoke alarm", "Paid parking on premises", "Coffee maker", "Wifi", "Iron", "Microwave", "Dryer", "TV", "Private entrance", "Dedicated workspace", "Cooking basics", "Kitchen", "Shampoo", "Essentials", "Carbon monoxide alarm", "Washer", "Stove"]
 
4
["Iron", "Kitchen", "Smoke alarm", "TV", "First aid kit", "Fire extinguisher", "Carbon monoxide alarm", "Washer", "Heating", "Wifi", "Hangers", "Free parking on premises", "Dedicated workspace", "Dryer", "Hair dryer"]
 
4
["Microwave", "Smoke alarm", "Dedicated workspace", "Refrigerator", "Dryer", "Luggage dropoff allowed", "Wifi", "Heating", "Fire extinguisher", "Essentials", "Carbon monoxide alarm", "Washer", "Long term stays allowed", "First aid kit", "Hangers"]
 
3
["Smoke alarm", "Keypad", "Dedicated workspace", "Dryer", "Hot water", "TV", "Kitchen", "Wifi", "Heating", "Iron", "Fire extinguisher", "Hair dryer", "Essentials", "Carbon monoxide alarm", "Washer", "First aid kit", "Hangers"]
 
3
["Oven", "Dryer", "Pool", "Hot water", "Stove", "Hair dryer", "Kitchen", "Microwave", "Long term stays allowed", "Indoor fireplace", "Shampoo", "Single level home", "Iron", "Washer", "Dishes and silverware", "Garden or backyard", "Coffee maker", "TV", "Wifi", "Cable TV", "Air conditioning", "Cooking basics", "Extra pillows and blankets", "Heating", "Carbon monoxide alarm", "Bed linens", "Refrigerator", "Host greets you", "Breakfast", "Luggage dropoff allowed", "First aid kit", "Hangers", "BBQ grill", "Dishwasher", "Dedicated workspace", "Smoke alarm", "Free parking on premises", "Patio or balcony", "Essentials"]
 
3
Other values (2626)
2676 

Length

Max length958
Median length611
Mean length431.3887857
Min length2

Characters and Unicode

Total characters1161730
Distinct characters70
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2582 ?
Unique (%)95.9%

Sample

1st row["Private entrance", "Long term stays allowed", "Garden or backyard", "TJs Tea Tree Tingle conditioner", "Hot water", "Wifi", "Washer", "Kitchen", "Dryer", "Shampoo", "Essentials", "Hair dryer", "Luggage dropoff allowed", "Shower gel", "Portable fans", "Free street parking", "Dedicated workspace", "Heating", "Carbon monoxide alarm", "Iron", "Extra pillows and blankets", "Refrigerator", "Fire extinguisher", "Hangers", "First aid kit", "Smoke alarm", "Microwave", "Coffee maker", "Dishes and silverware", "Single level home", "Free parking on premises", "Patio or balcony", "Bed linens"]
2nd row["Refrigerator", "Hot water", "Dishes and silverware", "Patio or balcony", "Bed linens", "Oven", "Hair dryer", "Extra pillows and blankets", "Long term stays allowed", "Hangers", "Smoke alarm", "Luggage dropoff allowed", "Coffee maker", "Wifi", "Iron", "Free street parking", "Microwave", "Keypad", "Dryer", "TV", "Private entrance", "Cable TV", "Dedicated workspace", "Cooking basics", "Kitchen", "Heating", "Fire extinguisher", "Shampoo", "Essentials", "Carbon monoxide alarm", "Washer", "Stove"]
3rd row["Hot water", "Hangers", "Wifi", "Host greets you", "Smoke alarm", "EV charger", "First aid kit", "Luggage dropoff allowed", "Dishes and silverware", "Hair dryer", "Essentials", "Coffee maker", "Dedicated workspace", "Heating", "Iron", "Long term stays allowed", "Free street parking", "Carbon monoxide alarm", "Garden or backyard", "Cleaning before checkout", "Cable TV", "TV", "Shampoo"]
4th row["Iron", "Dishes and silverware", "Shampoo", "Laundromat nearby", "Fire extinguisher", "Patio or balcony", "Essentials", "Wine glasses", "Microwave", "Free street parking", "Smoke alarm", "First aid kit", "Carbon monoxide alarm", "Clothing storage", "Cleaning products", "Pour-over coffee", "Dedicated workspace", "Hair dryer", "Dining table", "Hot water", "Bed linens", "TV", "Heating", "Hangers", "Wifi", "Private entrance", "Coffee maker", "Room-darkening shades", "Refrigerator", "Hot water kettle", "Mini fridge", "Host greets you", "Extra pillows and blankets"]
5th row["Refrigerator", "Hot water", "Lockbox", "Dishes and silverware", "Patio or balcony", "Bed linens", "Oven", "Hair dryer", "Extra pillows and blankets", "Hangers", "Smoke alarm", "Luggage dropoff allowed", "Ethernet connection", "Coffee maker", "Wifi", "Iron", "Free street parking", "Shower gel", "Host greets you", "First aid kit", "Microwave", "Breakfast", "Dryer", "TV", "Private entrance", "Cable TV", "Garden or backyard", "Single level home", "Dedicated workspace", "Free parking on premises", "Kitchen", "Heating", "Fire extinguisher", "Shampoo", "Essentials", "Carbon monoxide alarm", "Washer", "Baking sheet"]

Common Values

ValueCountFrequency (%)
["Refrigerator", "Hot water", "Dishes and silverware", "Patio or balcony", "Bed linens", "Oven", "Hair dryer", "Extra pillows and blankets", "Long term stays allowed", "Hangers", "Smoke alarm", "Paid parking on premises", "Coffee maker", "Wifi", "Iron", "Microwave", "Dryer", "TV", "Private entrance", "Dedicated workspace", "Cooking basics", "Kitchen", "Shampoo", "Essentials", "Carbon monoxide alarm", "Washer", "Stove"]4
 
0.1%
["Iron", "Kitchen", "Smoke alarm", "TV", "First aid kit", "Fire extinguisher", "Carbon monoxide alarm", "Washer", "Heating", "Wifi", "Hangers", "Free parking on premises", "Dedicated workspace", "Dryer", "Hair dryer"]4
 
0.1%
["Microwave", "Smoke alarm", "Dedicated workspace", "Refrigerator", "Dryer", "Luggage dropoff allowed", "Wifi", "Heating", "Fire extinguisher", "Essentials", "Carbon monoxide alarm", "Washer", "Long term stays allowed", "First aid kit", "Hangers"]3
 
0.1%
["Smoke alarm", "Keypad", "Dedicated workspace", "Dryer", "Hot water", "TV", "Kitchen", "Wifi", "Heating", "Iron", "Fire extinguisher", "Hair dryer", "Essentials", "Carbon monoxide alarm", "Washer", "First aid kit", "Hangers"]3
 
0.1%
["Oven", "Dryer", "Pool", "Hot water", "Stove", "Hair dryer", "Kitchen", "Microwave", "Long term stays allowed", "Indoor fireplace", "Shampoo", "Single level home", "Iron", "Washer", "Dishes and silverware", "Garden or backyard", "Coffee maker", "TV", "Wifi", "Cable TV", "Air conditioning", "Cooking basics", "Extra pillows and blankets", "Heating", "Carbon monoxide alarm", "Bed linens", "Refrigerator", "Host greets you", "Breakfast", "Luggage dropoff allowed", "First aid kit", "Hangers", "BBQ grill", "Dishwasher", "Dedicated workspace", "Smoke alarm", "Free parking on premises", "Patio or balcony", "Essentials"]3
 
0.1%
["Iron", "Kitchen", "Air conditioning", "TV", "Smoke alarm", "Fire extinguisher", "First aid kit", "Carbon monoxide alarm", "Heating", "Washer", "Wifi", "Hangers", "Dedicated workspace", "Dryer", "Hair dryer"]3
 
0.1%
["Smoke alarm", "Wifi", "First aid kit", "Heating", "Kitchen", "Dryer", "Hangers", "Hair dryer", "Iron", "TV", "Fire extinguisher", "Dedicated workspace", "Washer", "Air conditioning", "Carbon monoxide alarm"]3
 
0.1%
["Kitchen", "Air conditioning", "Smoke alarm", "First aid kit", "Fire extinguisher", "Carbon monoxide alarm", "Washer", "Hangers", "Heating", "Essentials", "Wifi", "Pool", "Dedicated workspace", "Dryer"]3
 
0.1%
["Refrigerator", "Free parking on premises", "Heating", "Hangers", "Hair dryer", "TV", "Dedicated workspace", "Stove", "Air conditioning", "Essentials", "Smoke alarm", "Dryer", "Lockbox", "Iron", "Shampoo", "Elevator", "Bed linens", "Kitchen", "Washer", "Carbon monoxide alarm", "Wifi", "Long term stays allowed", "Bathtub", "Hot water", "Oven", "Cable TV"]3
 
0.1%
["Oven", "Hot water", "Stove", "Hair dryer", "Kitchen", "Microwave", "Long term stays allowed", "Indoor fireplace", "Shampoo", "Iron", "Lock on bedroom door", "Dishes and silverware", "Free street parking", "Keypad", "Coffee maker", "Wifi", "Fire extinguisher", "Cooking basics", "Extra pillows and blankets", "Heating", "Carbon monoxide alarm", "Bed linens", "Refrigerator", "Luggage dropoff allowed", "Hangers", "Dishwasher", "Dedicated workspace", "Smoke alarm", "Free parking on premises", "Essentials"]3
 
0.1%
Other values (2621)2661
98.8%

Length

2022-10-25T03:38:16.632504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
alarm4977
 
3.6%
dryer4313
 
3.2%
parking3670
 
2.7%
free3572
 
2.6%
and3181
 
2.3%
tv2947
 
2.2%
on2738
 
2.0%
wifi2700
 
2.0%
essentials2634
 
1.9%
smoke2564
 
1.9%
Other values (311)103101
75.6%

Most occurring characters

ValueCountFrequency (%)
"146937
12.6%
133706
 
11.5%
e96218
 
8.3%
r78188
 
6.7%
a73425
 
6.3%
,70798
 
6.1%
i59823
 
5.1%
o57660
 
5.0%
n49380
 
4.3%
s43640
 
3.8%
Other values (60)351955
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter722617
62.2%
Other Punctuation218584
 
18.8%
Space Separator133706
 
11.5%
Uppercase Letter78746
 
6.8%
Close Punctuation2697
 
0.2%
Open Punctuation2697
 
0.2%
Decimal Number2351
 
0.2%
Dash Punctuation332
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e96218
13.3%
r78188
10.8%
a73425
10.2%
i59823
 
8.3%
o57660
 
8.0%
n49380
 
6.8%
s43640
 
6.0%
t40307
 
5.6%
l26413
 
3.7%
d25620
 
3.5%
Other values (14)171943
23.8%
Uppercase Letter
ValueCountFrequency (%)
H10044
12.8%
S7600
 
9.7%
C7181
 
9.1%
D7058
 
9.0%
F6940
 
8.8%
W4776
 
6.1%
E4323
 
5.5%
B4096
 
5.2%
P3627
 
4.6%
L3229
 
4.1%
Other values (13)19872
25.2%
Decimal Number
ValueCountFrequency (%)
0704
29.9%
1551
23.4%
2541
23.0%
9437
18.6%
3100
 
4.3%
512
 
0.5%
72
 
0.1%
62
 
0.1%
41
 
< 0.1%
81
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
"146937
67.2%
,70798
32.4%
\596
 
0.3%
/201
 
0.1%
&41
 
< 0.1%
:10
 
< 0.1%
.1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
]2693
99.9%
)4
 
0.1%
Open Punctuation
ValueCountFrequency (%)
[2693
99.9%
(4
 
0.1%
Space Separator
ValueCountFrequency (%)
133706
100.0%
Dash Punctuation
ValueCountFrequency (%)
-332
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin801363
69.0%
Common360367
31.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e96218
 
12.0%
r78188
 
9.8%
a73425
 
9.2%
i59823
 
7.5%
o57660
 
7.2%
n49380
 
6.2%
s43640
 
5.4%
t40307
 
5.0%
l26413
 
3.3%
d25620
 
3.2%
Other values (37)250689
31.3%
Common
ValueCountFrequency (%)
"146937
40.8%
133706
37.1%
,70798
19.6%
]2693
 
0.7%
[2693
 
0.7%
0704
 
0.2%
\596
 
0.2%
1551
 
0.2%
2541
 
0.2%
9437
 
0.1%
Other values (13)711
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1161730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
"146937
12.6%
133706
 
11.5%
e96218
 
8.3%
r78188
 
6.7%
a73425
 
6.3%
,70798
 
6.1%
i59823
 
5.1%
o57660
 
5.0%
n49380
 
4.3%
s43640
 
3.8%
Other values (60)351955
30.3%

minimum_nights
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.086520609
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2022-10-25T03:38:16.918214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile30
Maximum365
Range364
Interquartile range (IQR)4

Descriptive statistics

Standard deviation18.12863522
Coefficient of variation (CV)2.241833799
Kurtosis127.8365836
Mean8.086520609
Median Absolute Deviation (MAD)1
Skewness8.517712019
Sum21777
Variance328.6474148
MonotonicityNot monotonic
2022-10-25T03:38:17.147535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1900
33.4%
2653
24.2%
3319
 
11.8%
30243
 
9.0%
5105
 
3.9%
4101
 
3.8%
790
 
3.3%
1453
 
2.0%
3143
 
1.6%
1529
 
1.1%
Other values (27)157
 
5.8%
ValueCountFrequency (%)
1900
33.4%
2653
24.2%
3319
 
11.8%
4101
 
3.8%
5105
 
3.9%
69
 
0.3%
790
 
3.3%
92
 
0.1%
1019
 
0.7%
123
 
0.1%
ValueCountFrequency (%)
3652
 
0.1%
1831
 
< 0.1%
1803
 
0.1%
1201
 
< 0.1%
1101
 
< 0.1%
1006
 
0.2%
9024
0.9%
801
 
< 0.1%
608
 
0.3%
581
 
< 0.1%

maximum_nights
Real number (ℝ≥0)

Distinct88
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean631.4396584
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2022-10-25T03:38:17.429047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q131
median1124
Q31125
95-th percentile1125
Maximum10000
Range9999
Interquartile range (IQR)1094

Descriptive statistics

Standard deviation545.4670618
Coefficient of variation (CV)0.8638466948
Kurtosis30.26626838
Mean631.4396584
Median Absolute Deviation (MAD)1
Skewness1.762793388
Sum1700467
Variance297534.3155
MonotonicityNot monotonic
2022-10-25T03:38:17.727716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11251345
49.9%
365173
 
6.4%
30156
 
5.8%
90115
 
4.3%
28110
 
4.1%
29100
 
3.7%
18083
 
3.1%
6061
 
2.3%
745
 
1.7%
1441
 
1.5%
Other values (78)464
 
17.2%
ValueCountFrequency (%)
14
 
0.1%
210
 
0.4%
317
 
0.6%
410
 
0.4%
523
0.9%
67
 
0.3%
745
1.7%
84
 
0.1%
91
 
< 0.1%
1031
1.2%
ValueCountFrequency (%)
100001
 
< 0.1%
11251345
49.9%
11249
 
0.3%
111110
 
0.4%
10241
 
< 0.1%
10001
 
< 0.1%
9997
 
0.3%
7304
 
0.1%
7001
 
< 0.1%
6661
 
< 0.1%

has_availability
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
True
2662 
False
 
31
ValueCountFrequency (%)
True2662
98.8%
False31
 
1.2%
2022-10-25T03:38:18.724955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

availability_365
Real number (ℝ≥0)

ZEROS

Distinct333
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.1151133
Minimum0
Maximum365
Zeros366
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2022-10-25T03:38:18.927847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170
median172
Q3341
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)271

Descriptive statistics

Standard deviation135.6359605
Coefficient of variation (CV)0.7488936626
Kurtosis-1.484378512
Mean181.1151133
Median Absolute Deviation (MAD)133
Skewness0.1447715771
Sum487743
Variance18397.11379
MonotonicityNot monotonic
2022-10-25T03:38:19.225338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0366
 
13.6%
365290
 
10.8%
89123
 
4.6%
17991
 
3.4%
9082
 
3.0%
36474
 
2.7%
18069
 
2.6%
36349
 
1.8%
17838
 
1.4%
35937
 
1.4%
Other values (323)1474
54.7%
ValueCountFrequency (%)
0366
13.6%
113
 
0.5%
213
 
0.5%
314
 
0.5%
46
 
0.2%
54
 
0.1%
61
 
< 0.1%
76
 
0.2%
82
 
0.1%
94
 
0.1%
ValueCountFrequency (%)
365290
10.8%
36474
 
2.7%
36349
 
1.8%
36228
 
1.0%
36114
 
0.5%
36031
 
1.2%
35937
 
1.4%
35826
 
1.0%
35712
 
0.4%
35611
 
0.4%

number_of_reviews
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct307
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.89119941
Minimum0
Maximum711
Zeros400
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2022-10-25T03:38:19.518903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median20
Q368
95-th percentile227.8
Maximum711
Range711
Interquartile range (IQR)66

Descriptive statistics

Standard deviation79.31751773
Coefficient of variation (CV)1.499635452
Kurtosis9.279092791
Mean52.89119941
Median Absolute Deviation (MAD)20
Skewness2.616489209
Sum142436
Variance6291.268619
MonotonicityNot monotonic
2022-10-25T03:38:19.817048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0400
 
14.9%
1172
 
6.4%
2116
 
4.3%
373
 
2.7%
467
 
2.5%
658
 
2.2%
547
 
1.7%
741
 
1.5%
1041
 
1.5%
940
 
1.5%
Other values (297)1638
60.8%
ValueCountFrequency (%)
0400
14.9%
1172
6.4%
2116
 
4.3%
373
 
2.7%
467
 
2.5%
547
 
1.7%
658
 
2.2%
741
 
1.5%
834
 
1.3%
940
 
1.5%
ValueCountFrequency (%)
7111
< 0.1%
6241
< 0.1%
5971
< 0.1%
5851
< 0.1%
5761
< 0.1%
5411
< 0.1%
5271
< 0.1%
4881
< 0.1%
4801
< 0.1%
4611
< 0.1%

review_scores_rating
Real number (ℝ≥0)

MISSING

Distinct31
Distinct (%)1.4%
Missing414
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean95.74023695
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2022-10-25T03:38:20.037267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile86
Q194
median97
Q3100
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.848700368
Coefficient of variation (CV)0.06108926147
Kurtosis35.50820327
Mean95.74023695
Median Absolute Deviation (MAD)3
Skewness-4.329528794
Sum218192
Variance34.20729599
MonotonicityNot monotonic
2022-10-25T03:38:20.329764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
100571
21.2%
98265
9.8%
99261
9.7%
97235
8.7%
96199
 
7.4%
95146
 
5.4%
94104
 
3.9%
93104
 
3.9%
9273
 
2.7%
9069
 
2.6%
Other values (21)252
9.4%
(Missing)414
15.4%
ValueCountFrequency (%)
202
 
0.1%
401
 
< 0.1%
6011
0.4%
631
 
< 0.1%
651
 
< 0.1%
692
 
0.1%
702
 
0.1%
731
 
< 0.1%
752
 
0.1%
782
 
0.1%
ValueCountFrequency (%)
100571
21.2%
99261
9.7%
98265
9.8%
97235
8.7%
96199
 
7.4%
95146
 
5.4%
94104
 
3.9%
93104
 
3.9%
9273
 
2.7%
9147
 
1.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
False
1631 
True
1062 
ValueCountFrequency (%)
False1631
60.6%
True1062
39.4%
2022-10-25T03:38:20.616577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

reviews_per_month
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct597
Distinct (%)26.0%
Missing400
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean1.874348016
Minimum0.01
Maximum20.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2022-10-25T03:38:20.817072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.07
Q10.39
median1.18
Q32.71
95-th percentile5.764
Maximum20.04
Range20.03
Interquartile range (IQR)2.32

Descriptive statistics

Standard deviation1.968969414
Coefficient of variation (CV)1.0504823
Kurtosis5.508210864
Mean1.874348016
Median Absolute Deviation (MAD)0.93
Skewness1.84551262
Sum4297.88
Variance3.876840554
MonotonicityNot monotonic
2022-10-25T03:38:21.117052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0530
 
1.1%
0.0726
 
1.0%
124
 
0.9%
0.1124
 
0.9%
0.0621
 
0.8%
0.0319
 
0.7%
0.0819
 
0.7%
0.1419
 
0.7%
0.2119
 
0.7%
0.0418
 
0.7%
Other values (587)2074
77.0%
(Missing)400
 
14.9%
ValueCountFrequency (%)
0.012
 
0.1%
0.025
 
0.2%
0.0319
0.7%
0.0418
0.7%
0.0530
1.1%
0.0621
0.8%
0.0726
1.0%
0.0819
0.7%
0.0918
0.7%
0.112
 
0.4%
ValueCountFrequency (%)
20.041
< 0.1%
11.861
< 0.1%
10.961
< 0.1%
10.931
< 0.1%
10.671
< 0.1%
10.631
< 0.1%
10.321
< 0.1%
10.271
< 0.1%
101
< 0.1%
9.941
< 0.1%

Interactions

2022-10-25T03:38:06.703329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:43.766138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:46.094240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:48.309163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:50.498338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:52.696880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:54.893502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:57.800744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:59.997344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:02.202081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:04.501615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:06.896004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:44.025944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:46.289132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:48.493509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:50.684205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:52.879431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:55.090705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:57.984076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:00.190555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:02.389732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:04.690395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:07.109164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:44.247465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:46.500279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:48.703824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:50.899939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:53.093781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:55.300441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:58.198532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:00.404985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:02.601320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:04.902289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:07.311197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:44.476113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:46.701018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:48.897183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:51.093851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:53.287647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:55.498191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:58.393719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:00.603563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:02.820145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:05.095736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:07.525901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:44.679196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:46.906686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:49.096350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:51.290933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:53.489030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:55.697389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:58.589931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:00.800137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:03.035048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:05.297641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:07.745178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:44.875817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:47.098538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:49.287338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:51.483981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:53.674536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:55.891679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:58.783980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:00.991775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:03.261956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:05.491837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:07.987490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:45.080537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-25T03:37:49.495344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:51.694674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:53.884952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:56.713632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:58.992260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:01.201081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:03.483580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:05.697181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:08.191979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:45.278607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:47.494974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:49.690486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:51.907880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:54.077513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:56.898650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:59.186050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:01.391060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:03.694214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:05.892321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:08.398494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:45.483865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:47.707368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:49.900209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:52.101144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:54.285340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:57.124213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:59.397448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:01.596259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:03.897310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:06.099093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:08.603917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:45.695388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:47.905061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:50.095782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:52.296237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:54.484237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:57.349347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:59.593613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:01.799605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:04.100033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:06.303967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:08.806106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:45.897327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:48.105561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:50.298554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:52.497901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:54.694795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:57.586004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:37:59.795158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:01.999749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:04.301624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-25T03:38:06.500958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-25T03:38:21.334532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-25T03:38:21.631304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-25T03:38:21.895439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-25T03:38:22.183515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-25T03:38:22.458894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-25T03:38:22.700540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-25T03:38:09.228575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-25T03:38:09.970792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-25T03:38:10.444017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-25T03:38:10.602228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idhost_idhost_namehost_sincehost_response_timehost_response_ratehost_acceptance_ratehost_is_superhosthost_verificationshost_identity_verifiedneighbourhoodroom_typeaccommodatesbedroomsbedsamenitiesminimum_nightsmaximum_nightshas_availabilityavailability_365number_of_reviewsreview_scores_ratinginstant_bookablereviews_per_month
01265149735Laurel10/29/09within an hour100%98%t['email', 'phone', 'facebook', 'reviews', 'kba']tMenlo ParkEntire home/apt11.01.0["Private entrance", "Long term stays allowed", "Garden or backyard", "TJs Tea Tree Tingle conditioner", "Hot water", "Wifi", "Washer", "Kitchen", "Dryer", "Shampoo", "Essentials", "Hair dryer", "Luggage dropoff allowed", "Shower gel", "Portable fans", "Free street parking", "Dedicated workspace", "Heating", "Carbon monoxide alarm", "Iron", "Extra pillows and blankets", "Refrigerator", "Fire extinguisher", "Hangers", "First aid kit", "Smoke alarm", "Microwave", "Coffee maker", "Dishes and silverware", "Single level home", "Free parking on premises", "Patio or balcony", "Bed linens"]3365t35615198.0t1.13
139342127367Annette5/18/10within a few hours100%40%f['email', 'phone', 'reviews', 'offline_government_id', 'kba', 'government_id']tPacificaEntire home/apt32.02.0["Refrigerator", "Hot water", "Dishes and silverware", "Patio or balcony", "Bed linens", "Oven", "Hair dryer", "Extra pillows and blankets", "Long term stays allowed", "Hangers", "Smoke alarm", "Luggage dropoff allowed", "Coffee maker", "Wifi", "Iron", "Free street parking", "Microwave", "Keypad", "Dryer", "TV", "Private entrance", "Cable TV", "Dedicated workspace", "Cooking basics", "Kitchen", "Heating", "Fire extinguisher", "Shampoo", "Essentials", "Carbon monoxide alarm", "Washer", "Stove"]30365t365290.0f0.02
240560174725Megan7/20/10NaNNaN48%t['email', 'phone', 'reviews', 'jumio', 'offline_government_id', 'government_id']tEast Palo AltoPrivate room21.01.0["Hot water", "Hangers", "Wifi", "Host greets you", "Smoke alarm", "EV charger", "First aid kit", "Luggage dropoff allowed", "Dishes and silverware", "Hair dryer", "Essentials", "Coffee maker", "Dedicated workspace", "Heating", "Iron", "Long term stays allowed", "Free street parking", "Carbon monoxide alarm", "Garden or backyard", "Cleaning before checkout", "Cable TV", "TV", "Shampoo"]1100t36513398.0f1.06
344504195645Maggie8/8/10within an hour100%100%t['email', 'phone', 'facebook', 'reviews', 'offline_government_id', 'kba', 'selfie', 'government_id', 'identity_manual', 'work_email']tBurlingameEntire home/apt21.01.0["Iron", "Dishes and silverware", "Shampoo", "Laundromat nearby", "Fire extinguisher", "Patio or balcony", "Essentials", "Wine glasses", "Microwave", "Free street parking", "Smoke alarm", "First aid kit", "Carbon monoxide alarm", "Clothing storage", "Cleaning products", "Pour-over coffee", "Dedicated workspace", "Hair dryer", "Dining table", "Hot water", "Bed linens", "TV", "Heating", "Hangers", "Wifi", "Private entrance", "Coffee maker", "Room-darkening shades", "Refrigerator", "Hot water kettle", "Mini fridge", "Host greets you", "Extra pillows and blankets"]328t17915097.0f1.20
445287200479Diane And Mike8/13/10within an hour100%94%t['email', 'phone', 'reviews', 'kba']tBurlingameEntire home/apt2NaN1.0["Refrigerator", "Hot water", "Lockbox", "Dishes and silverware", "Patio or balcony", "Bed linens", "Oven", "Hair dryer", "Extra pillows and blankets", "Hangers", "Smoke alarm", "Luggage dropoff allowed", "Ethernet connection", "Coffee maker", "Wifi", "Iron", "Free street parking", "Shower gel", "Host greets you", "First aid kit", "Microwave", "Breakfast", "Dryer", "TV", "Private entrance", "Cable TV", "Garden or backyard", "Single level home", "Dedicated workspace", "Free parking on premises", "Kitchen", "Heating", "Fire extinguisher", "Shampoo", "Essentials", "Carbon monoxide alarm", "Washer", "Baking sheet"]31125t17725798.0f2.07
545444174725Megan7/20/10NaNNaN48%t['email', 'phone', 'reviews', 'jumio', 'offline_government_id', 'government_id']tEast Palo AltoPrivate room21.01.0["Iron", "Shampoo", "Smoke alarm", "Private living room", "Carbon monoxide alarm", "Heating", "Hangers", "Wifi", "Essentials", "Free parking on premises", "Lockbox", "Dedicated workspace", "Hair dryer"]17t3654698.0f0.38
655301261070Shannon10/13/10NaNNaN100%t['email', 'phone', 'reviews', 'manual_offline', 'jumio', 'government_id']tMenlo ParkEntire home/apt31.01.0["Private entrance", "Long term stays allowed", "Garden or backyard", "Hot water", "Washer", "Wifi", "Dryer", "Ethernet connection", "Shampoo", "Hair dryer", "Essentials", "Kitchen", "Luggage dropoff allowed", "Lockbox", "Free street parking", "Carbon monoxide alarm", "Heating", "Dedicated workspace", "Dishwasher", "Iron", "Stove", "Oven", "Extra pillows and blankets", "Body soap", "Refrigerator", "Cable TV", "Fire extinguisher", "TV", "Hangers", "First aid kit", "Smoke alarm", "Microwave", "Baking sheet", "Ceiling fan", "Coffee maker", "Cooking basics", "Dishes and silverware", "Conditioner", "Central air conditioning", "Single level home", "Free parking on premises", "Patio or balcony", "Bed linens"]30730t3314398.0f0.37
763146308176Jennifer12/2/10NaNNaNNaNf['email', 'phone']fMenlo ParkEntire home/apt83.06.0["Free parking on premises", "Heating", "Hangers", "Hair dryer", "TV", "Dedicated workspace", "Indoor fireplace", "Fire extinguisher", "Air conditioning", "Essentials", "Smoke alarm", "Dryer", "Luggage dropoff allowed", "Lockbox", "Iron", "Shampoo", "Bed linens", "Kitchen", "Washer", "First aid kit", "Carbon monoxide alarm", "Extra pillows and blankets", "Wifi", "Long term stays allowed", "Hot water", "Private entrance", "Cable TV"]230t3641NaNf0.04
872211101491Jennifer3/31/10within an hour100%100%t['email', 'phone', 'reviews']fPortola ValleyEntire home/apt83.05.0["Iron", "Dishes and silverware", "Shampoo", "Fire extinguisher", "Keypad", "Single level home", "Patio or balcony", "Essentials", "Microwave", "Free street parking", "Cable TV", "Smoke alarm", "Extra pillows and blankets", "First aid kit", "Carbon monoxide alarm", "Garden or backyard", "Ethernet connection", "Dedicated workspace", "High chair", "Hair dryer", "Hot tub", "Stove", "Bathtub", "Dishwasher", "Hot water", "Bed linens", "TV", "Air conditioning", "Heating", "Hangers", "Wifi", "Pool", "Free parking on premises", "Oven", "Private entrance", "Coffee maker", "Room-darkening shades", "Refrigerator", "Children\u2019s books and toys", "Game console", "Kitchen", "Long term stays allowed", "Washer", "Pack \u2019n Play/travel crib", "Cooking basics", "Dryer"]3190t26210398.0t0.88
983584127367Annette5/18/10within a few hours100%40%f['email', 'phone', 'reviews', 'offline_government_id', 'kba', 'government_id']tPacificaEntire home/apt21.01.0["Refrigerator", "Hot water", "Dishes and silverware", "Oven", "Hair dryer", "Long term stays allowed", "Hangers", "Smoke alarm", "Luggage dropoff allowed", "Coffee maker", "Wifi", "Iron", "Free street parking", "Microwave", "Keypad", "Dryer", "TV", "Dedicated workspace", "Cooking basics", "Kitchen", "Fire extinguisher", "Shampoo", "Essentials", "Carbon monoxide alarm", "Washer"]30290t365690.0f0.09

Last rows

idhost_idhost_namehost_sincehost_response_timehost_response_ratehost_acceptance_ratehost_is_superhosthost_verificationshost_identity_verifiedneighbourhoodroom_typeaccommodatesbedroomsbedsamenitiesminimum_nightsmaximum_nightshas_availabilityavailability_365number_of_reviewsreview_scores_ratinginstant_bookablereviews_per_month
26834726876257058531Yusu2/1/16within an hour96%98%f['email', 'phone', 'offline_government_id', 'government_id']tSouth San FranciscoShared room11.01.0["Iron", "Breakfast", "Dishes and silverware", "Shampoo", "Luggage dropoff allowed", "Fire extinguisher", "Essentials", "Freezer", "HDTV with Netflix", "Microwave", "Free street parking", "Smoke alarm", "First aid kit", "Carbon monoxide alarm", "Smart lock", "Dedicated workspace", "Hair dryer", "Body soap", "Dining table", "Stove", "Bathtub", "Hot water", "Conditioner", "Portable fans", "Heating", "Hangers", "Wifi", "Free parking on premises", "Oven", "Shower gel", "Coffee maker", "Room-darkening shades", "Refrigerator", "Children\u2019s books and toys", "Kitchen", "Long term stays allowed", "Hot water kettle", "Washer", "Cooking basics", "Dryer"]11125t79160.0f1.0
268447270034209426739Grace8/14/18within an hour100%100%f['phone', 'offline_government_id', 'selfie', 'government_id', 'identity_manual']tPacificaEntire home/apt21.01.0["Smoke alarm", "Dryer", "Hot water", "Kitchen", "Private entrance", "Wifi", "Heating", "Essentials", "Carbon monoxide alarm", "Washer"]1365t32100.0t2.0
268547274251226555948Gi'Angelo11/19/18within an hour100%95%t['email', 'phone', 'reviews', 'offline_government_id', 'government_id']tDaly CityEntire home/apt74.04.0["Iron", "Dishes and silverware", "Shampoo", "Paid parking garage on premises \u2013 1 space", "Fire extinguisher", "Keypad", "Essentials", "Freezer", "Wine glasses", "Microwave", "Free street parking", "Toaster", "Smoke alarm", "First aid kit", "Carbon monoxide alarm", "Cleaning products", "Dedicated workspace", "Baking sheet", "Hair dryer", "Dining table", "Stove", "Bathtub", "Dishwasher", "Hot water", "Bed linens", "TV", "Conditioner", "Heating", "Hangers", "Wifi", "Oven", "Shower gel", "Rice maker", "Coffee maker", "Room-darkening shades", "Body wash body soap", "Refrigerator", "Kitchen", "Clothing storage: closet", "Hot water kettle", "Cooking basics"]3090t00NaNfNaN
268647274359226555948Gi'Angelo11/19/18within an hour100%95%t['email', 'phone', 'reviews', 'offline_government_id', 'government_id']tDaly CityPrivate room11.01.0["Oven", "Hot water", "Stove", "Hair dryer", "Body wash body soap", "Kitchen", "Microwave", "Shampoo", "Wine glasses", "Iron", "Lock on bedroom door", "Dishes and silverware", "Paid parking garage on premises \u2013 1 space", "Free street parking", "Keypad", "Coffee maker", "TV", "Wifi", "Conditioner", "Fire extinguisher", "Freezer", "Cooking basics", "Heating", "Carbon monoxide alarm", "Bed linens", "Toaster", "Refrigerator", "Rice maker", "First aid kit", "Hangers", "Mini fridge", "Clothing storage: closet", "Dishwasher", "Dedicated workspace", "Smoke alarm", "Baking sheet", "Dining table", "Room-darkening shades", "Cleaning products", "Hot water kettle", "Essentials", "Shower gel"]30120t00NaNfNaN
268747274445226555948Gi'Angelo11/19/18within an hour100%95%t['email', 'phone', 'reviews', 'offline_government_id', 'government_id']tDaly CityPrivate room11.01.0["Oven", "Hot water", "Stove", "Hair dryer", "Kitchen", "Body wash body soap", "Microwave", "Shampoo", "Iron", "Lock on bedroom door", "Dishes and silverware", "Free street parking", "Keypad", "Coffee maker", "TV", "Wifi", "Essentials", "Conditioner", "Fire extinguisher", "Freezer", "Cooking basics", "Heating", "Carbon monoxide alarm", "Bed linens", "Refrigerator", "Rice maker", "Mini fridge", "Hangers", "First aid kit", "Dishwasher", "Dedicated workspace", "Smoke alarm", "Baking sheet", "Room-darkening shades", "Paid parking garage on premises \u2013 1 space", "Shower gel"]30120t00NaNfNaN
268847274637226555948Gi'Angelo11/19/18within an hour100%95%t['email', 'phone', 'reviews', 'offline_government_id', 'government_id']tDaly CityPrivate room11.01.0["Iron", "Dishes and silverware", "Shampoo", "Paid parking garage on premises \u2013 1 space", "Fire extinguisher", "Keypad", "Essentials", "Freezer", "Wine glasses", "Microwave", "Free street parking", "Toaster", "Smoke alarm", "First aid kit", "Carbon monoxide alarm", "Cleaning products", "Lock on bedroom door", "Dedicated workspace", "Baking sheet", "Hair dryer", "Dining table", "Stove", "Dishwasher", "Hot water", "Bed linens", "TV", "Conditioner", "Heating", "Hangers", "Wifi", "Oven", "Shower gel", "Rice maker", "Coffee maker", "Room-darkening shades", "Body wash body soap", "Refrigerator", "Kitchen", "Clothing storage: closet", "Hot water kettle", "Mini fridge", "Cooking basics"]30120t1780NaNfNaN
268947274772226555948Gi'Angelo11/19/18within an hour100%95%t['email', 'phone', 'reviews', 'offline_government_id', 'government_id']tDaly CityPrivate room11.01.0["Body wash body soap", "Refrigerator", "Hot water", "Mini fridge", "Dishes and silverware", "Bed linens", "Oven", "Hair dryer", "Freezer", "Hangers", "Smoke alarm", "Clothing storage: closet", "Stainless steel stove", "Coffee maker", "Wifi", "Lock on bedroom door", "Iron", "Free street parking", "Shower gel", "Room-darkening shades", "First aid kit", "Microwave", "Baking sheet", "Dishwasher", "Keypad", "Toaster", "TV", "Wine glasses", "Conditioner", "Dedicated workspace", "Rice maker", "Cooking basics", "Kitchen", "Heating", "Fire extinguisher", "Shampoo", "Hot water kettle", "Essentials", "Cleaning products", "Carbon monoxide alarm", "Dining table", "Paid parking garage on premises \u2013 1 space"]30120t1500NaNfNaN
269047291853368763236Rolf & Noah As Manager9/22/20within an hour100%93%f['phone', 'offline_government_id', 'selfie', 'government_id', 'identity_manual']tUnincorporated AreasEntire home/apt42.02.0["Smoke alarm", "TV", "Heating", "Iron", "Hot water", "Fire extinguisher", "Hangers", "First aid kit", "Carbon monoxide alarm", "Wifi", "Dryer", "Kitchen", "Washer", "Hot tub", "Essentials", "Private entrance", "Indoor fireplace", "Shampoo", "Free parking on premises"]510t3020NaNtNaN
26914729501741574587Enrique And Vienna Of Q Management8/15/15within an hour86%98%f['email', 'phone', 'reviews', 'kba']fSouth San FranciscoEntire home/apt42.02.0["Mini fridge", "Hair dryer", "Hangers", "Smoke alarm", "Smart lock", "Coffee maker", "Wifi", "Iron", "Microwave", "Breakfast", "Dryer", "Private entrance", "Dedicated workspace", "Cooking basics", "Indoor fireplace", "Kitchen", "Heating", "Fire extinguisher", "Shampoo", "Essentials", "Carbon monoxide alarm", "Washer"]129t3470NaNtNaN
269247299925188426286Liz5/7/18NaNNaNNaNf['email', 'phone', 'offline_government_id', 'selfie', 'government_id']tSan MateoPrivate room1NaN1.0["Kitchen", "Gym", "Essentials", "Hot water", "Shampoo", "Washer", "Wifi", "Smoke alarm", "First aid kit", "Fire extinguisher", "Carbon monoxide alarm", "Pool", "Lock on bedroom door", "Dryer"]1365t1470NaNfNaN